Integrative modelling reveals mechanisms linking productivity and plant species richness

Journal name:
Nature
Volume:
529,
Pages:
390–393
Date published:
DOI:
doi:10.1038/nature16524
Received
Accepted
Published online

How ecosystem productivity and species richness are interrelated is one of the most debated subjects in the history of ecology1. Decades of intensive study have yet to discern the actual mechanisms behind observed global patterns2, 3. Here, by integrating the predictions from multiple theories into a single model and using data from 1,126 grassland plots spanning five continents, we detect the clear signals of numerous underlying mechanisms linking productivity and richness. We find that an integrative model has substantially higher explanatory power than traditional bivariate analyses. In addition, the specific results unveil several surprising findings that conflict with classical models4, 5, 6, 7. These include the isolation of a strong and consistent enhancement of productivity by richness, an effect in striking contrast with superficial data patterns. Also revealed is a consistent importance of competition across the full range of productivity values, in direct conflict with some (but not all) proposed models. The promotion of local richness by macroecological gradients in climatic favourability, generally seen as a competing hypothesis8, is also found to be important in our analysis. The results demonstrate that an integrative modelling approach leads to a major advance in our ability to discern the underlying processes operating in ecological systems.

At a glance

Figures

  1. Comparison between low-dimension (top panel) and high-dimension (bottom panel) examinations of data.
    Figure 1: Comparison between low-dimension (top panel) and high-dimension (bottom panel) examinations of data.

    A, Raw bivariate plot of above-ground productivity and species richness in 1-m2 plots (n = 1,126). Different sites are represented in the graphs by different colours, assigned by mean site richness from low (yellow) to high (red). B, Plots ac visualize site level partial relationships indicated by corresponding letters in Fig. 2 (n = 39 sites). Plots df visualize plot level partial relationships indicated by corresponding letters in Fig. 2 (n = 1,126 1-m2 plots). Units are standardized residual deviations from predicted partial scores.

  2. Structural equation model representing connections between productivity and richness supported by the data.
    Figure 2: Structural equation model representing connections between productivity and richness supported by the data.

    ‘Biomass’ refers to total above-ground accumulated biomass. Letters correspond to partial plots shown in Fig. 1B. Solid arrows represent positive effects, dashed arrows represent negative effects. For the site-level submodel, test statistic= 13.518, with 13 model degrees of freedom and P = 0.409 (indicating close model-data fit). For the plot-level submodel, robust test statistic= 21.907, with 16 model degrees of freedom and P = 0.146 (again indicating close model-data fit). Relative effect sizes presented in Table 1.

  3. Structural equation meta-model showing hypothesized probabilistic expectations based on literature related to the productivity–diversity debate.
    Extended Data Fig. 1: Structural equation meta-model showing hypothesized probabilistic expectations based on literature related to the productivity–diversity debate.

    Solid lines represent expected positive effects, dashed lines represent expected negative effects. Literature and meta-model development are discussed in the Supplementary Information. Specific implementations of this generalized model for particular cases will probably differ in detail as appropriate for the situation and available data.

Tables

  1. Model variables and their indicators*
    Extended Data Table 1: Model variables and their indicators*
  2. Results of model dimensionality evaluations
    Extended Data Table 2: Results of model dimensionality evaluations
  3. Basic information on the study sites included in the final analyses
    Extended Data Table 3: Basic information on the study sites included in the final analyses

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Author information

Affiliations

  1. US Geological Survey, Wetland and Aquatic Research Center, 700 Cajundome Boulevard, Lafayette, Louisiana 70506, USA

    • James B. Grace
  2. Department of Biology, 206 Winston Hall, Wake Forest University, Box 7325 Reynolda Station, Winston-Salem, North Carolina 27109, USA

    • T. Michael Anderson
  3. Ecology, Evolution, and Behavior, University of Minnesota, 1987 Upper Buford Circle, St Paul, Minnesota 55108, USA

    • Eric W. Seabloom,
    • Elizabeth T. Borer &
    • Eric M. Lind
  4. Department of Wildland Resources and the Ecology Center, Utah State University, 5230 Old Main, Logan, Utah 84322, USA

    • Peter B. Adler
  5. Department of Physiological Diversity, Helmholtz Center for Environmental Research – UFZ, Permoserstrasse 15, 04318 Leipzig, Germany

    • W. Stanley Harpole
  6. German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, D-04103 Leipzig, Germany

    • W. Stanley Harpole
  7. Martin Luther University Halle-Wittenberg, Am Kirchtor 1, 06108 Halle (Saale), Germany

    • W. Stanley Harpole
  8. Ecology and Biodiversity Group, Department of Biology, Utrecht University, Padualaan 8, Utrecht 3584 CH, The Netherlands

    • Yann Hautier
  9. Institute for Chemistry and Biology of the Marine Environment, University of Oldenburg, Schleusenstrasse 1, Wilhelmshaven D-26381, Germany

    • Helmut Hillebrand
  10. Institute of Ecology and Earth Sciences, University of Tartu, Lai 40, Tartu 51005, Estonia

    • Meelis Pärtel
  11. School of Environmental and Forest Sciences, University of Washington, Box 354115, Seattle, Washington 98195-4115, USA

    • Jonathan D. Bakker
  12. School of Natural Sciences, Zoology, Trinity College Dublin, The University of Dublin, Dublin 2, Ireland

    • Yvonne M. Buckley
  13. Department of Biological Sciences, Imperial College London, Silwood Park, Ascot, Berkshire SL5 7PY, UK

    • Michael J. Crawley
  14. Department of Zoology, University of Wisconsin, 430 Lincoln Drive, Madison, Wisconsin 53706, USA

    • Ellen I. Damschen &
    • John L. Orrock
  15. Department of Ecology and Evolutionary Biology, UCB 334, University of Colorado, Boulder, Colorado 80309, USA

    • Kendi F. Davies &
    • Brett A. Melbourne
  16. Grassland Soil and Water Research Laboratory, United States Department of Agriculture Agricultural Research Service, 808 East Blackland Road, Temple, Texas 76502, USA

    • Philip A. Fay
  17. #15 Queensland University of Technology, School of Earth, Environment and Biological Sciences, Brisbane, Queensland 4001, Australia

    • Jennifer Firn
  18. Department of Entomology, University of Maryland, College Park, 4112 Plant Sciences, College Park, Maryland 20742, USA

    • Daniel S. Gruner
  19. Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK

    • Andy Hector
  20. School of Biological Sciences, 348 Manter Hall, University of Nebraska, Lincoln, Nebraska 68588, USA

    • Johannes M. H. Knops
  21. Department of Integrative Biology, University of Guelph, Guelph, Ontario N1G 2W1, Canada

    • Andrew S. MacDougall
  22. Department of Ecology, Environment, and Evolution, La Trobe University, Bundoora, Victoria 3083, Australia

    • John W. Morgan
  23. CSIRO Land and Water, Private Bag 5, Wembley, Western Australia, 6913, Australia

    • Suzanne M. Prober
  24. Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, Colorado 80526, USA

    • Melinda D. Smith

Contributions

E.W.S., E.T.B., W.S.H. and E.M.L. are Nutrient Network coordinators. J.B.G. and T.M.A. developed and framed the research questions. T.M.A., E.W.S., E.T.B., P.B.A., W.S.H., Y.H., H.H., J.D.B., Y.M.B., M.J.C., E.I.D., K.F.D., P.A.F., J.F., D.S.G., A.H., J.M.H.K., A.S.M., B.A.M., J.W.M., J.L.O., S.M.P. and M.D.S. collected data used in this analysis. T.M.A. assembled the data and performed initial analyses. J.B.G. analysed the data and wrote the paper with contributions and input from all authors.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:

Author details

Extended data figures and tables

Extended Data Figures

  1. Extended Data Figure 1: Structural equation meta-model showing hypothesized probabilistic expectations based on literature related to the productivity–diversity debate. (93 KB)

    Solid lines represent expected positive effects, dashed lines represent expected negative effects. Literature and meta-model development are discussed in the Supplementary Information. Specific implementations of this generalized model for particular cases will probably differ in detail as appropriate for the situation and available data.

Extended Data Tables

  1. Extended Data Table 1: Model variables and their indicators* (152 KB)
  2. Extended Data Table 2: Results of model dimensionality evaluations (124 KB)
  3. Extended Data Table 3: Basic information on the study sites included in the final analyses (426 KB)

Supplementary information

PDF files

  1. Supplementary Information (740 KB)

    This file contains Supplementary Materials and Methods, Supplementary Tables 1-2, Supplementary References and Supplementary Acknowledgements.

Text files

  1. Supplementary Data 1 (29 KB)

    This file contains the computer code that accompanies the paper.

Other

  1. Supplementary Data 2 (222 KB)

    This file contains the plot-level data set.

  2. Supplementary Data 3 (9.4 KB)

    This file contains the site-level dataset.

Additional data